Fracton models, a collection of exotic gapped lattice Hamiltonians recently discovered in three spatial dimensions, contain some “topological” features: They support fractional bulk excitations ...(dubbed fractons) and a ground-state degeneracy that is robust to local perturbations. However, because previous fracton models have been defined and analyzed only on a cubic lattice with periodic boundary conditions, it is unclear to what extent a notion of topology is applicable. In this paper, we demonstrate that theX-cube model, a prototypical type-I fracton model, can be defined on general three-dimensional manifolds. Our construction revolves around the notion of a singular compact total foliation of the spatial manifold, which constructs a lattice from intersecting stacks of parallel surfaces called leaves. We find that the ground-state degeneracy depends on the topology of the leaves and the pattern of leaf intersections. We further show that such a dependence can be understood from a renormalization group transformation for theX-cube model, wherein the system size can be changed by adding or removing 2D layers of topological states. Our results lead to an improved definition of fracton phase and bring to the fore the topological nature of fracton orders.
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Until now there has been no fundamental theory applicable for biodegradable metals (BMs). First, this paper optimizes the definition of BMs given in 2014. Second, the dual criteria of ...biodegradability and biocompatibility are proposed for BMs, and all metallic elements in the periodic table with accessible data are screened on the basis of these criteria. Regarding biodegradability, electrode potential, reactivity series, galvanic series, Pilling–Bedworth ratio, and Pourbaix diagrams are all adopted as parameters to classify the degradable and nondegradable nature of a material, especially in a physiological environment. Considering the biocompatibility at different levels, cellular biocompatibility, tissue biocompatibility, and human/clinical related biocompatibility parameters are put forward to comprehensively evaluate the biosafety of BMs. Third, for the material design of BMs, mechanical properties, chemical properties, physical properties and biological properties should be considered and balanced to guarantee that the degradation behavior of BMs match well with a tissue regeneration/repair procedure as the function of time and spatial location. Besides the selected metallic elements, some nonmetallic elements are selected as suitable alloying elements for BMs. Finally, five classification/research directions for future BMs are proposed: biodegradable pure metals, crystalline alloys, bulk metallic glasses, high entropy alloys, and metal matrix composites.
The fundamental theory of biodegradable metals are clarified in this review article, with the definition of biodegradable metals, biodegradability, and biocompatibility dual criteria for metallic elements in the periodic table being regarded as biodegradable metals. Key properties needed to be considered for materials design of biodegradable metals and future research and development directions for biodegradable metals are proposed.
•To the best of our knowledge, Mask-CNN is the first end-to-end model that selects deep convolutional descriptors for object recognition, especially for fine-grained image recognition.•We present a ...novel and efficient part-based three-stream model for fine-grained recognition. By discarding the fully connected layers, the proposed M-CNN is computationally efficient (cf. Table 1 and Table 4 in experiments). Additionally, comparing with state-of-the-art methods, M-CNN has smaller feature dimensionality. Beyond those, it achieves the highest classification accuracy on CUB200-2011 and Birdsnap among published methods.•The part localization performance of the proposed model outperforms other part-based finegrained approaches which requires additional bounding boxes. In particular, M-CNN is 12.76% higher than state-of-the-art for head localization on CUB200-2011.
Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we prove that selecting useful deep descriptors contributes well to fine-grained image recognition. Specifically, a novel Mask-CNN model without the fully connected layers is proposed. Based on the part annotations, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate weighted object/part masks for selecting useful and meaningful convolutional descriptors. After that, a three-stream Mask-CNN model is built for aggregating the selected object- and part-level descriptors simultaneously. Thanks to discarding the parameter redundant fully connected layers, our Mask-CNN has a small feature dimensionality and efficient inference speed by comparing with other fine-grained approaches. Furthermore, we obtain a new state-of-the-art accuracy on two challenging fine-grained bird species categorization datasets, which validates the effectiveness of both the descriptor selection scheme and the proposed Mask-CNN model.
Metal-organic frameworks (MOFs), a novel class of porous crystalline materials, have drawn enormous attention. Due to the inherent porosity and presence of both metal and organic moieties, MOF-based ...materials are naturally suitable as versatile precursors and sacrificial templates for a wide variety of metal/carbon-based nanostructured materials, such as metal oxides, metal carbides, metal sulfides and their composites. Recent developments in MOF-derived hollow nanostructures with well-defined interior voids and low density have revealed their extensive capabilities and thus give enhanced performance for energy storage and conversion. In this review, we summarize the recent progress in the fabrication of MOF-derived hollow materials and their applications for energy storage, particularly for lithium-ion batteries, sodium-ion batteries, lithium-Se batteries, lithium-sulfur batteries and supercapacitors. The superiorities of MOF-derived hollow materials are highlighted, and major challenges or opportunities for future research on them for electrochemical energy storage are also discussed, with prospective solutions in the light of current progress in MOF-derived hollow nanostructures.
The recent progress and major challenges/opportunities of MOF-derived hollow materials for energy storage are summarized in this review, particularly for lithium-ion batteries, sodium-ion batteries, lithium-Se batteries, lithium-sulfur batteries and supercapacitor applications.
Three-dimensional (3D) topological superconductors (TScs) protected by time-reversal (T ) symmetry are characterized by gapless Majorana cones on their surface. Free-fermion phases with this symmetry ...(class DIII) are indexed by an integer ν , of which ν=1 is realized by the B phase of superfluid He3 . Previously, it was believed that the surface must be gapless unless time-reversal symmetry is broken. Here, we argue that a fully symmetric and gapped surface is possible in the presence of strong interactions, if a special type of topological order appears on the surface. The topological order realizes T symmetry in an anomalous way, one that is impossible to achieve in purely two dimensions. For odd νTScs , the surface topological order must be non-Abelian. We propose the simplest non-Abelian topological order that contains electronlike excitations, SO(3)6 , with four quasiparticles, as a candidate surface state. Remarkably, this theory has a hidden T invariance that, however, is broken in any two-dimensional realization. By explicitly constructing an exactly soluble Walker-Wang model, we show that it can be realized at the surface of a short-ranged entangled 3D fermionic phase protected by T symmetry, with bulk electrons transforming as Kramers pairs, i.e. T2=−1 under time reversal. We also propose an Abelian theory, the semion-fermion topological order, to realize an even νTSc surface, for which an explicit model is derived using a coupled-layer construction. We argue that this is related to the ν=2TSc , and we use this to build candidate surface topological orders for ν=4 and ν=8TScs . The latter is equivalent to the three-fermion state, which is the surface topological order of a Z2 bosonic topological phase protected by T invariance. One particular consequence of this equivalence is that a ν=16TSc admits a trivially gapped T -symmetric surface.
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Nanoparticles for photothermal therapy: Real‐time temperature monitoring is critical to reduce the nonspecific damage during photothermal therapy (PTT); however, PTT agents that can emit ...temperature‐related signals are rare and limited to few inorganic nanoparticles. We herein synthesize a semiconducting polymer nanococktail (SPNCT) that can not only convert photo‐energy to heat but also emit temperature‐correlated luminescence after cessation of light excitation. Such an afterglow luminescence of the SPNCT detects tumors more sensitively than fluorescence as a result of the elimination of tissue autofluorescence, while its temperature‐dependent nature allows tumor temperature to be optically monitored under near‐infrared (NIR) laser irradiation. Thus, SPNCT represents the first organic optical nanosystem that enables optical‐imaging guided PTT without real‐time light excitation.
Afterglow, a cocktail: A semiconducting polymer nanococktail (SPNCT) with temperature‐correlated afterglow luminescence is designed for imaging‐guided photothermal therapy. The afterglow intensity of the SPNCT has a good linear correlation with temperature, allowing the photothermal temperature of the tumor to be monitored under NIR laser irradiation.
Symmetry fractionalization describes the fascinating phenomena that excitations in a 2D topological system can transform under symmetry in a fractional way. For example in fractional quantum Hall ...systems, excitations can carry fractional charges while the electrons making up the system have charge one. An important question is to understand what symmetry fractionalization (SF) patterns are possible given different types of topological order and different global symmetries. A lot of progress has been made recently in classifying the SF patterns, providing deep insight into the strongly correlated experimental signatures of systems like spin liquids and topological insulators. We review recent developments on this topic. First, it was shown that the SF patterns need to satisfy some simple consistency conditions. More interestingly, it was realized that some seemingly consistent SF patterns are actually ‘anomalous’, i.e. they cannot be realized in strictly 2D systems. We review various methods that have been developed to detect such anomalies. Applying such an understanding to 2D spin liquid allows one to enumerate all potentially realizable SF patterns and propose numerical and experimental probing methods to distinguish them. On the other hand, the anomalous SF patterns were shown to exist on the surface of 3D systems and reflect the nontrivial order in the 3D bulk. We review examples of this kind where the bulk states are topological insulators, topological superconductors, or have other symmetry protected topological orders.
Deep Label Distribution Learning With Label Ambiguity Gao, Bin-Bin; Xing, Chao; Xie, Chen-Wei ...
IEEE transactions on image processing,
2017-June, 2017-Jun, 2017-6-00, 20170601, Volume:
26, Issue:
6
Journal Article
Peer reviewed
Convolutional neural networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its ...success. However, it is difficult to collect sufficient training images with precise labels in some domains, such as apparent age estimation, head pose estimation, multilabel classification, and semantic segmentation. Fortunately, there is ambiguous information among labels, which makes these tasks different from traditional classification. Based on this observation, we convert the label of each image into a discrete label distribution, and learn the label distribution by minimizing a Kullback-Leibler divergence between the predicted and ground-truth label distributions using deep ConvNets. The proposed deep label distribution learning (DLDL) method effectively utilizes the label ambiguity in both feature learning and classifier learning, which help prevent the network from overfitting even when the training set is small. Experimental results show that the proposed approach produces significantly better results than the state-of-the-art methods for age estimation and head pose estimation. At the same time, it also improves recognition performance for multi-label classification and semantic segmentation tasks.
Green electricity is an effective approach to mitigate greenhouse gas emissions without damaging economic development. One effective measure to boost its development is to increase the awareness of ...residents about sustainable development and to increase their willingness to pay (WTP). This study applies a contingent valuation method to investigate the WTP of residents in Tianjin, China for green electricity. It uses a logit model to test whether the respondents have WTP. In addition to the factors affecting the respondents’ viewpoints frequently discussed in previous studies, this paper has also considered the influence of high prevalence of respiratory disease. Furthermore, it employs a multiple regression model to identify the factors that significantly affected the WTP. According to a sample of 468 respondents, most residents have positive WTP with an average value of CNY 32.63 per month. The main driving factors for a positive WTP are knowledge of renewable energy, belief towards the government, behavior, education, history of respiratory disease and others. The factors affecting the value of WTP include income, belief, disease, gender and age, and the first three factors often have positive influences while the last one has negative impact. Furthermore, males tend to have a higher WTP than females.
•Influence of poor air quality and prevalence of respiratory disease are considered.•Majority of respondents have positive attitudes to developing green electricity.•Knowledge, belief, behavior, education and health condition are positively factors.•Income, gender and age affect the value of Willingness to Pay significantly.•Improving the transparency of governance is an effective way to increase WTP.
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